Papers with Persian language
DadmaTools: Natural Language Processing Toolkit for Persian Language (2022.naacl-demo)
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| Challenge: | Existing tools for Persian language processing are based on conventional non-neural models and do not take full advantage of the latest developments. |
| Approach: | They propose to use a Python neural pipeline for Persian text processing tasks . they use 'parsBERT' to fine-tune the Python pipeline using the PerDT dataset . |
| Outcome: | The proposed toolkit can achieve state-of-the-art performance on multiple NLP tasks. |
ParsiNLU: A Suite of Language Understanding Challenges for Persian (2021.tacl-1)
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Daniel Khashabi, Arman Cohan, Siamak Shakeri, Pedram Hosseini, Pouya Pezeshkpour, Malihe Alikhani, Moin Aminnaseri, Marzieh Bitaab, Faeze Brahman, Sarik Ghazarian, Mozhdeh Gheini, Arman Kabiri, Rabeeh Karimi Mahabagdi, Omid Memarrast, Ahmadreza Mosallanezhad, Erfan Noury, Shahab Raji, Mohammad Sadegh Rasooli, Sepideh Sadeghi, Erfan Sadeqi Azer, Niloofar Safi Samghabadi, Mahsa Shafaei, Saber Sheybani, Ali Tazarv, Yadollah Yaghoobzadeh
| Challenge: | Despite progress in natural language understanding, most progress is concentrated on resource-rich languages like English . despite high-quality benchmarks, there are few available NLU datasets for Persian language . |
| Approach: | They propose a benchmark for Persian language that includes a range of language understanding tasks . they present their results on monolingual and multilingual pre-trained language models . |
| Outcome: | The proposed benchmarks compare human performance with monolingual and multilingual models on Persian language with high quality evaluation datasets. |
ParsTranslit: Truly Versatile Tajik-Farsi Transliteration (2026.findings-eacl)
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| Challenge: | Despite significant similarities between the two written standards, script differences hinder simple one-to-one mapping, hindering written communication and interaction between Tajikistan and its Persian-speaking “siblings”. |
| Approach: | They propose to use a sequence-to-sequence model to convert between two scripts in a Persian-speaking country using two datasets. |
| Outcome: | The proposed model achieves chrF++ and Normalized CER scores of 87.91 and 0.05 from Farsi to Tajik and 92.28 and 0.04 from Tajikistan to Farsis. |
Advancing Persian LLM Evaluation (2025.findings-naacl)
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Sara Bourbour Hosseinbeigi, Behnam Rohani, Mostafa Masoudi, Mehrnoush Shamsfard, Zahra Saaberi, Mostafa Karimi Manesh, Mohammad Amin Abbasi
| Challenge: | Existing evaluation approaches for large language models in low-resource languages like Persian lack comprehensive frameworks, limiting their ability to assess models’ performance over a wide range of tasks requiring considerable cultural and contextual knowledge. |
| Approach: | They propose to provide two new benchmarks to assess models' performance over a wide range of tasks requiring considerable cultural and contextual knowledge. |
| Outcome: | The proposed benchmarks challenge the current state-of-the-art models’ abilities in a variety of Persian language comprehension tasks while reducing data contamination while providing an accurate assessment of Persian LLMs. |
Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT (2024.lrec-main)
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Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi, Nesa Abbasi, Mohammad Hadi Babalou, Ali Edalat, Sepehr Kamahi, Samin Mahdizadeh Sani, Nikoo Naghavian, Danial Namazifard, Pouya Sadeghi, Yadollah Yaghoobzadeh
| Challenge: | a new study examines the efficacy of large language models (LLMs) for Persian . ChatGPT and LLMs have shown remarkable performance in English, but their efficiency for low-resource languages remains an open question. |
| Approach: | They present a benchmarking study of large language models (LLMs) for Persian . they focus on GPT-3.5-turbo, but also GPT-4 and OpenChat-3.5 . |
| Outcome: | The proposed model performs better in Persian than other low-resource languages . the study is the first comprehensive benchmarking of large language models . |
Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction (2020.lrec-1)
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Preni Golazizian, Behnam Sabeti, Seyed Arad Ashrafi Asli, Zahra Majdabadi, Omid Momenzadeh, Reza Fahmi
| Challenge: | Existing methods for emotion extraction and sentiment analysis produce invalid results due to the use of irony. |
| Approach: | They propose to use emoji prediction to fine tune a model using hand labeled tweets with irony tags. |
| Outcome: | The proposed method outperforms the state-of-the-art method on Persian dataset with an accuracy of 83.1% and offers strong baseline for further research in Persian language. |
HarfoSokhan: A Comprehensive Parallel Dataset for Transitions between Persian Colloquial and Formal Variations (2026.eacl-long)
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Hamid Jahad Sarvestani, Vida Ramezanian, Saee Saadat, Neda Taghizadeh Serajeh, Maryam Sadat Razavi Taheri, Shohreh Kasaei, MohammadAmin Fazli, Ehsaneddin Asgari
| Challenge: | A wide array of NLP/NLU models have been developed for the Persian language but performance drops when applied to the colloquial form of Persian. |
| Approach: | They propose to use a large-scale colloquial to formal Persian parallel dataset to train a GPT2 model that exhibited remarkable proficiency in colloqual to informal text style transfer. |
| Outcome: | The proposed dataset outperforms OpenAI’s GPT-3.5-turbo model and a leading rule-based system in colloquial to formal Persian conversion. |
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian (2020.lrec-1)
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Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, Omid Momenzadeh
| Challenge: | Existing deep learning approaches require huge amounts of data to be trained properly. |
| Approach: | They propose to use Persian as a model to choose the samples for annotation instead of labeling the whole dataset. |
| Outcome: | The proposed models achieve the baseline performance with a significantly lower amount of labeled data. |
ManaTTS Persian: a recipe for creating TTS datasets for lower resource languages (2025.naacl-long)
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| Challenge: | a new text-to-speech system is needed for visual impairments and the visually impaired . a text-based system is not available for all users, and is therefore limited to a limited audience. |
| Approach: | They propose to use ManaTTS, the most extensive publicly accessible Persian corpus . they use a fully transparent, MIT-licensed pipeline to collect transcribed speech datasets . |
| Outcome: | The proposed framework is the most extensive publicly accessible single-speaker Persian corpus . it includes tools for sentence tokenization, bounded audio segmentation, and forced alignment method . |